Dynamic Graph Neural Networks for Sequential Recommendation
release_74kljncl4nfefeehuqjg6g47du
by
Mengqi Zhang, Shu Wu, Xueli Yu, Qiang Liu, Liang Wang
2021
Abstract
Modeling user preference from his historical sequences is one of the core
problems of sequential recommendation. Existing methods in this field are
widely distributed from conventional methods to deep learning methods. However,
most of them only model users' interests within their own sequences and ignore
the dynamic collaborative signals among different user sequences, making it
insufficient to explore users' preferences. We take inspiration from dynamic
graph neural networks to cope with this challenge, modeling the user sequence
and dynamic collaborative signals into one framework. We propose a new method
named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which
connects different user sequences through a dynamic graph structure, exploring
the interactive behavior of users and items with time and order information.
Furthermore, we design a Dynamic Graph Recommendation Network to extract user's
preferences from the dynamic graph. Consequently, the next-item prediction task
in sequential recommendation is converted into a link prediction between the
user node and the item node in a dynamic graph. Extensive experiments on three
public benchmarks show that DGSR outperforms several state-of-the-art methods.
Further studies demonstrate the rationality and effectiveness of modeling user
sequences through a dynamic graph.
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